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Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also...

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Detalles Bibliográficos
Autores principales: Li, Qun, Liu, Linlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208962/
https://www.ncbi.nlm.nih.gov/pubmed/35733571
http://dx.doi.org/10.1155/2022/3500592
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author Li, Qun
Liu, Linlin
author_facet Li, Qun
Liu, Linlin
author_sort Li, Qun
collection PubMed
description In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.
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spelling pubmed-92089622022-06-21 Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation Li, Qun Liu, Linlin Comput Intell Neurosci Research Article In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized. Hindawi 2022-06-13 /pmc/articles/PMC9208962/ /pubmed/35733571 http://dx.doi.org/10.1155/2022/3500592 Text en Copyright © 2022 Qun Li and Linlin Liu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Qun
Liu, Linlin
Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
title Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
title_full Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
title_fullStr Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
title_full_unstemmed Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
title_short Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
title_sort artificial intelligence-based semisupervised self-training algorithm in pathological tissue image segmentation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208962/
https://www.ncbi.nlm.nih.gov/pubmed/35733571
http://dx.doi.org/10.1155/2022/3500592
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